Update app.py

app.py

@@ -1,226 +1,3 @@
-import gradio as gr
-from gradio_client import Client, handle_file
-import random
-import os
-
-# API client for the external Space
-space_client = Client("prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast")
-
-LORA_STYLES = [
-    'Multiple-Angles',
-    'Photo-to-Anime',
-    'Anime-V2',
-    'Light-Migration',
-    'Upscaler',
-    'Style-Transfer',
-    'Manga-Tone',
-    'Anything2Real',
-    'Fal-Multiple-Angles',
-    'Polaroid-Photo',
-    'Unblur-Anything',
-    'Midnight-Noir-Eyes-Spotlight',
-    'Hyper-Realistic-Portrait',
-    'Ultra-Realistic-Portrait',
-    'Pixar-Inspired-3D',
-    'Noir-Comic-Book',
-    'Any-light',
-    'Studio-DeLight',
-    'Cinematic-FlatLog',
-]
-MAX_SEED = 2**31 - 1
-
-def encode_image_to_gallery_dict(image_path):
-    if not image_path or not isinstance(image_path, str):
-        return None
-    try:
-        file_data = handle_file(image_path)
-        return {"image": file_data, "caption": ""}
-    except Exception as e:
-        print(f"无法读取图片: {image_path}: {e}")
-        return None
-
-def infer(
-    image,
-    prompt,
-    lora_adapter,
-    seed,
-    randomize_seed,
-    guidance_scale,
-    steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    # Process input image(s) as a list of dict, each of form {"image": {"data": ...}}
-    images_input = []
-    if image is not None:
-        if isinstance(image, list):  # Gradio Gallery
-            for im in image:
-                img_obj = encode_image_to_gallery_dict(im)
-                if img_obj:
-                    images_input.append(img_obj)
-                else:
-                    print(f"警告: 路径无效或无法读取: {im}")
-        else:
-            img_obj = encode_image_to_gallery_dict(image)
-            if img_obj:
-                images_input.append(img_obj)
-            else:
-                print(f"警告: 路径无效或无法读取: {image}")
-
-    if len(images_input) == 0:
-        print("未检测到有效图片，未能上传图片。")
-        return None, seed
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    print("[调用API] space_client.predict 输入参数：")
-    print(f"  images count: {len(images_input)}")
-    print(f"  prompt: {prompt}")
-    print(f"  lora_adapter: {lora_adapter}")
-    print(f"  seed: {seed}")
-    print(f"  randomize_seed: {randomize_seed}")
-    print(f"  guidance_scale: {guidance_scale}")
-    print(f"  steps: {steps}")
-
-    try:
-        # Gradio Space expects list of {"image": {"data": <b64 str>}} elements (see AppError in prompt)
-        result = space_client.predict(
-            images=images_input,
-            prompt=prompt,
-            lora_adapter=lora_adapter,
-            seed=float(seed),
-            randomize_seed=bool(randomize_seed),
-            guidance_scale=float(guidance_scale),
-            steps=float(steps),
-            api_name="/infer",
-        )
-        print(f"[调用API] space_client.predict 返回值: {result}")
-        # result可能不是元组形式，需增加健壮性
-        if isinstance(result, dict):
-            # 新API只返回dict
-            image_info = result
-            seed_used = result.get("seed", seed)
-        elif isinstance(result, (tuple, list)) and len(result) == 2:
-            image_info, seed_used = result
-        else:
-            print(f"[错误] space_client.predict 返回类型未知: {type(result)}，内容: {result}")
-            return None, seed
-
-        # 检查image_info是否包含url/path
-        if isinstance(image_info, dict):
-            img_url = image_info.get("url") or image_info.get("path") or None
-            # 获取base64图片（如果有）
-            if img_url is None and "data" in image_info:
-                # 返回base64图片（data格式）
-                return f"data:image/png;base64,{image_info['data']}", seed_used
-            return img_url, seed_used
-        else:
-            print(f"[错误] image_info 格式异常: {image_info}")
-            return None, seed
-    except Exception as e:
-        import traceback
-        traceback.print_exc()
-        print(f"[调用API] 调用接口异常: {e}")
-        if "Could not process uploaded images" in str(e):
-            print(
-                "\n[错误] 上传图片处理失败。请确保图片文件有效且未损坏。\n"
-                "可以尝试重新选择图片或修改图片格式。"
-            )
-        else:
-            print("\n[错误] 调用推理服务失败，请稍后重试或联系开发者。")
-        return None, seed
-
-examples = [
-    [None, "Astronaut in jungle, anime style", "Photo-to-Anime", 0, True, 1.0, 4],
-    [None, "A delicious ceviche cheesecake slice", "Style-Transfer", 0, True, 1.0, 4],
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks() as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # 图像编辑 API Demo (基于 prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast)")
-
-        with gr.Row():
-            image = gr.Image(
-                label="上传图片",
-                sources=["upload"],
-                type="filepath",
-                elem_id="input-image"
-            )
-        with gr.Row():
-            prompt = gr.Text(
-                label="编辑描述（Prompt）",
-                placeholder="请输入图片编辑描述...",
-            )
-
-        with gr.Row():
-            lora_adapter = gr.Dropdown(
-                label="编辑风格（Style）",
-                choices=LORA_STYLES,
-                value="Photo-to-Anime"
-            )
-
-        run_button = gr.Button("执行编辑", scale=1, variant="primary")
-
-        result = gr.Image(label="结果图片", show_label=True)
-
-        with gr.Accordion("高级设置", open=False):
-            seed = gr.Slider(
-                label="随机种子",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="随机种子", value=True)
-
-            guidance_scale = gr.Slider(
-                label="引导强度(Guidance Scale)",
-                minimum=0.1,
-                maximum=10.0,
-                step=0.1,
-                value=1.0,
-            )
-
-            steps = gr.Slider(
-                label="推理步数(Steps)",
-                minimum=1,
-                maximum=50,
-                step=1,
-                value=4,
-            )
-
-        # Only show example text/inputs, but avoid file path errors (set image to None)
-        gr.Examples(
-            examples=examples,
-            inputs=[image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps],
-            label="示例",
-        )
-
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            image,
-            prompt,
-            lora_adapter,
-            seed,
-            randomize_seed,
-            guidance_scale,
-            steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    # See: https://prithivmlmods-qwen-image-edit-2511-loras-fast.hf.space
-    # To use SSR, remove 'ssr_mode=False' (can help with speed in some settings)
-    # To create a public link, set share=True
-    demo.launch(css=css, ssr_mode=True, share=True)
+from gradio_client import Client
+client = Client("prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast")
+print(client.view_api())